Sentiment Analysis in Microblogs Using HMMs with Syntactic and Sentimental Information

Abstract

In this paper, we propose an approach for sentiment analysis in microblogs that learns patterns of syntactic and sentimental word transitions. Because sentences are sequences of words, we can more accurately analyze sentiments by properly modeling the sequential patterns of words in sentimental sentences. However, most previous research has focused on just extending feature sets using n-grams, POS tags, polarity lexicons, etc., without considering sequential patterns. Our proposed approach first identifies groups of words that have similar syntactic and sentimental roles, called SIGs (similar syntactic and sentimental information groups). We then build HMMs using the SIGs as hidden states for the initialization. The SIGs function as the prior knowledge of formative elements of sentimental sentences for HMMs. By using the SIGs, HMMs can start with informative hidden states and more precisely model the transition patterns of words in sentimental sentences with robust probability estimation. For the performance evaluation, we compare the proposed approach with existing ones using HCR dataset. The result shows that the proposed approach outperforms the previous ones in various performance measures.

Sentiment Analysis for Troll Detection on Weibo

“…Sentiment analysis is widely used for mining subjective information in online posts. In [14], Kim, et al, use hidden Markov models with syntactic and sentiment information for sentiment analysis of Twitter data. This differs from classic approaches that use 𝑛-grams and polarity lexicons, as they group words based on similar syntactic and sentiment groups (SIG), then build HMMs, where the SIGs define the hidden states.…”